{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5e5a61d-071d-452d-abe8-76115e8f92e8",
   "metadata": {},
   "source": [
    "# reading and viewer nexus file data\n",
    "\n",
    "Below are some useful snippets of code for loading in data from a nexus file, checking for keywords within the nexus file structure, and then extracting data from a path within the nexus file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5ba34bff-e512-469c-bc8e-0ae4877fb8d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nexusformat.nexus as nx\n",
    "import numpy as np\n",
    "from nexusformat.nexus.tree import NXlink,NXdata,NXentry\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Load in example dataset\n",
    "nxspath='/dls/science/groups/das/ExampleData/i07/fast_rsm_example_data/si40043/i07-593344.nxs'\n",
    "nxs_data=nx.nxload(nxspath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "a4496e7a-627d-44ff-b87a-aef39e60b3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Define a checking function to printout the data paths that contain a certain string\n",
    "def checkforkey(keystring,data,level=0,keypath='',level_limit=3):\n",
    "    '''\n",
    "    function for iteratively checking through nexus file for certain key names up to a certain depth level\n",
    "    '''\n",
    "    if level==level_limit:\n",
    "        return\n",
    "    for key in data.keys():\n",
    "        keypathin=\"/\".join([keypath,key])\n",
    "\n",
    "        if key.find(keystring)==0:\n",
    "            x=4\n",
    "            print(keypathin)\n",
    "        goodlist=[NXdata,NXentry]\n",
    "        if type(data[key])not in goodlist:\n",
    "            pass\n",
    "        elif (len(data[key].keys())>=1):\n",
    "            inlevel=level+1\n",
    "            checkforkey(keystring,data[key],level=inlevel,keypath=keypathin)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "15d4cbcd-6286-4f20-aa6e-58fa4ac72f0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/entry/EXCALIBUR/diff1delta_value\n",
      "/entry/EXCALIBUR/diff1delta_value_set\n",
      "/entry/EXCALIBUR_Region_1_sum/diff1delta_value\n",
      "/entry/EXCALIBUR_Region_1_sum/diff1delta_value_set\n",
      "/entry/EXCALIBUR_Region_2_sum/diff1delta_value\n",
      "/entry/EXCALIBUR_Region_2_sum/diff1delta_value_set\n",
      "/entry/EXCALIBUR_sum/diff1delta_value\n",
      "/entry/EXCALIBUR_sum/diff1delta_value_set\n",
      "/entry/adc1/diff1delta_value\n",
      "/entry/adc1/diff1delta_value_set\n",
      "/entry/adc2/diff1delta_value\n",
      "/entry/adc2/diff1delta_value_set\n",
      "/entry/adc3/diff1delta_value\n",
      "/entry/adc3/diff1delta_value_set\n",
      "/entry/adc4/diff1delta_value\n",
      "/entry/adc4/diff1delta_value_set\n",
      "/entry/d5i/diff1delta_value\n",
      "/entry/d5i/diff1delta_value_set\n",
      "/entry/d5xminus/diff1delta_value\n",
      "/entry/d5xminus/diff1delta_value_set\n",
      "/entry/d5xplus/diff1delta_value\n",
      "/entry/d5xplus/diff1delta_value_set\n",
      "/entry/d5yminus/diff1delta_value\n",
      "/entry/d5yminus/diff1delta_value_set\n",
      "/entry/d5yplus/diff1delta_value\n",
      "/entry/d5yplus/diff1delta_value_set\n",
      "/entry/hkl/diff1delta_value\n",
      "/entry/hkl/diff1delta_value_set\n"
     ]
    }
   ],
   "source": [
    "#example of checking for string in datapaths up to level 4\n",
    "checkforkey('diff1delta',nxs_data,level_limit=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "00168438-9d73-4032-85d1-f9e0f0775152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcd42382200>]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mpyYTG+pvcTJxBZUPERFxqYOnKpiUkcnmQ8UA3HVVZx4d0Qsfr4v+xg9xEyofIiLiMityj/Lw4hxKK2sI9vPm5dsSub5PpNWxxMVUPkREpMlVVteS/tF2/r5hPwDJcSG8nppM+xA/i5OJFVQ+RESkSe07UU5aRiZbj5QA8Luru/DQsJ54e2rM0lqpfIiISJN5f/MRpi3ZQpmjhrb+3sy8vT/X9oqwOpZYTOVDREQaXWV1Lc98sI2Mrw4AcFmnUF6b0J/oYI1ZROVDREQa2e7jZaTNy2RHQSk2G6Rd0437h3bHS2MW+TeVDxERaTRLsw7xh6W5VFTVEhbgwyvj+jOoe7jVsaSZUfkQEZGLdqaqlqfey2XhxkMApHRpx2vj+xMR5GtxMmmOVD5EROSi7Cos5ffzMtl1rAybDaYM6c7k67rj6WGzOpo0UyofIiJyQYwxLNp0iCffzaWy2kl4oJ3Xxvfniq5hVkeTZk7lQ0REGqzcUcMTy3JZknUYgEHdw3hlXH/CAuwWJxN3oPIhIiINsv1oCZMyMtl9vBwPGzw4rCf3Xt0VD41Z5DypfIiIyHkxxjD/64PMeH8rjhonUUG+zJqQxGWdQ62OJm5G5UNERH5WaWU105fm8v7mIwBc0zOcmbf3J7SNj8XJxB2pfIiIyE/KPVzMpIxM9p2swNPDxiPDe3L3oC4as8gFU/kQEZGzMsbw/77cz3MfbKeq1kn7ED9mTUhiQMe2VkcTN6fyISIiP1J8ppppS3L4aEsBAEN7R/LybQmE+GvMIhdP5UNEROrZfLCISfMzOXjqDN6eNh4b2ZvfXNkJm01jFmkcKh8iIgJ8O2b52+f7eGH5dqprDbGhfsyekExibIjV0aSFUfkQERGKKqp4eHEOH28rBGBkfBQvjE0g2M/b4mTSEql8iIi0cpkHTjM5I4vDRWfw8fTg8Rt786vLO2rMIk1G5UNEpJVyOg3//ekeXlqZR43T0LGdP3NSk4lvH2x1NGnhVD5ERFqhU+VVPLgwm0/yjgNwY0I06WP6EeirMYs0PZUPEZFW5uu9p7hvfhYFJZX4eHnw9E19mXBZrMYs4jIqHyIirYTTaZi7bjczP95JrdPQJbwNc1KT6R0dZHU0aWVUPkREWoETZQ4eeCebT3edAOCWpPY8NzqeNnY9DYjr6V4nItLCbdh9kikLsjhW6sDX24NnRsVz24AOGrOIZVQ+RERaqFqn4fU1u5i1ehdOA90jApgzMZkekYFWR5NWTuVDRKQFOlZayf0Lsvli90kAbr+kAzNujsfPx9PiZCIqHyIiLc5nu05w/ztZnCirwt/Hk+dGxzMmuYPVsUTqqHyIiLQQNbVOXl21izlr8zEGekUFMjs1mW4RAVZHE6lH5UNEpAUoKK7kvgVZfL33FACpA+N48sY++HprzCLNj8qHiIib+yTvGA8u3Myp8ioC7F48P6YfNyfGWB1L5JxUPkRE3FR1rZOX/5XHm+v2ANA3JojZqcl0DmtjcTKRn6byISLihg4XneG++Vls2n8agF+ndGT6Db01ZhG3oPIhIuJmVm0r5MFFmyk+U02grxcvjk1gZL9oq2OJnDeVDxERN1FV4+TFFTv462d7AUjsEMzrE5KJa+dvcTKRhvFoyM7p6elceumlBAYGEhERwejRo8nLy6u3zzXXXIPNZqv3c8899zRqaBGR1ubgqQpue3NDXfH4zZWdWXTPFSoe4pYa9MrHunXrSEtL49JLL6Wmpobp06czbNgwtm3bRps2/3eA0913380zzzxTd9rfXw8OEZELtSL3KA8vzqG0soZgP29evi2R6/tEWh1L5II1qHysWLGi3um3336biIgINm3axODBg+vO9/f3JyoqqnESioi0Uo6aWp7/cDt/37AfgKS4EF6fkESHtvoHnbi3Bo1dfqi4uBiA0NDQeufPmzePsLAw4uPjmTZtGhUVFee8DIfDQUlJSb0fEZHWbt+JcsbO/aKuePzu6i4s/F2Kioe0CBd8wKnT6eT+++/nyiuvJD4+vu781NRUOnbsSExMDDk5OTz66KPk5eWxZMmSs15Oeno6M2bMuNAYIiItzgc5R3jsn1soc9TQ1t+bmbf359peEVbHEmk0NmOMuZBfvPfee1m+fDmfffYZHTqc+wuL1qxZw5AhQ8jPz6dr164/2u5wOHA4HHWnS0pKiI2Npbi4mKCgoAuJJiLiliqra3nmg21kfHUAgEs7tWXWhCSig/0sTiby80pKSggODj6v5+8LeuVj0qRJfPDBB6xfv/4niwfAwIEDAc5ZPux2O3a7/UJiiIi0GLuPl5E2L5MdBaXYbPD7a7rywNAeeHle1HRcpFlqUPkwxjB58mSWLl3K2rVr6dy588/+TnZ2NgDR0foAHBGRs1mWdZjpS7dQUVVLuzY+vDKuP4N7hFsdS6TJNKh8pKWlkZGRwbvvvktgYCAFBQUABAcH4+fnx+7du8nIyOCGG26gXbt25OTk8MADDzB48GASEhKa5AaIiLirM1W1PP3eVt7ZeBCAy7uEMmt8EhFBvhYnE2laDTrmw2aznfX8t956izvvvJODBw/yy1/+ktzcXMrLy4mNjeWWW27h8ccfP+/jNxoyMxIRcVe7CktJy8hkZ2EZNhvcd1137hvSHU+Ps/+dFWnumuyYj5/rKbGxsaxbt64hFyki0uos2niQJ9/dypnqWsID7bw2rj9XdAuzOpaIy+i7XUREXKTcUcMT7+ayJPMwAIO6hzHz9v6EB+qge2ldVD5ERFxgR0EJafMy2X28HA8bTL2+B7+/phseGrNIK6TyISLShIwxLPjmIE+/txVHjZPIIDuzxicxsEs7q6OJWEblQ0SkiZQ5api+ZAvvbT4CwDU9w/nzbYm0C9CYRVo3lQ8RkSaQe7iYSRmZ7DtZgaeHjYeH9+S3g7pozCKCyoeISKMyxvC/X+7n2Q+2U1XrJCbYl9dTkxjQMfTnf1mklVD5EBFpJCWV1Tz2zxw+2vLtBzAO7R3By7clEuLvY3EykeZF5UNEpBHkHCoiLSOTg6fO4O1p49ERvbjrqs7n/HBGkdZM5UNE5CIYY3jr832kL99Oda2hQ1s/Zqcm0z82xOpoIs2WyoeIyAUqrqjm4cWb+de2QgBG9I3iT7cmEOznbXEykeZN5UNE5AJkHjjN5IwsDhedwcfTgz/8oje/TumoMYvIeVD5EBFpAKfT8NfP9vDiijxqnIaO7fyZk5pMfPtgq6OJuA2VDxGR83S6vIoHF21mzY5jANyYEE36mH4E+mrMItIQKh8iIufhm32nuG9+FkeLK/Hx8uCpm/qQelmcxiwiF0DlQ0TkJzidhrnrdjPz453UOg1dwtowOzWZPjFBVkcTcVsqHyIi53CizMED72Tz6a4TANyS1J7nRsfTxq4/nSIXQ48gEZGz2LD7JFMWZHGs1IGvtwfP3BzPbZd00JhFpBGofIiIfE+t0zB7TT6vrd6J00C3iADemJhMj8hAq6OJtBgqHyIi/3astJL7F2Tzxe6TANw2oAMzRvXF30d/KkUakx5RIiLAZ7tOcP872Zwoc+Dv48lzo+MZk9zB6lgiLZLKh4i0ajW1Tl5bvYvZn+RjDPSKCmR2ajLdIgKsjibSYql8iEirVVBcyX0Lsvh67ykAJlwWx1M39cHX29PiZCItm8qHiLRKa/OOMXXhZk6VV9HGx5P0sQncnBhjdSyRVkHlQ0RalepaJzM/3snctbsB6BMdxJyJyXQOa2NxMpHWQ+VDRFqNI0VnmDw/i037TwPw65SOTL+ht8YsIi6m8iEircKqbYU8tHgzRRXVBNq9+NOtCdzQL9rqWCKtksqHiLRoVTVOXlyxg79+theAhA7BzJ6QTFw7f4uTibReKh8i0mIdPFXBpPlZbD5YBMBvruzMoyN7YvfSmEXESiofItIircgt4JHFmymprCHI14uXb0tkWN8oq2OJCCofItLCOGpqSf9oB29/sQ+ApLgQXp+QRIe2GrOINBcqHyLSYuw/Wc6kjCy2HC4G4HeDu/DQ8J54e3pYnExEvk/lQ0RahA9zjvLYP3ModdTQ1t+bP9+eyHW9Iq2OJSJnofIhIm6tsrqW5z7cxv9+eQCASzu1ZdaEJKKD/SxOJiLnovIhIm5rz/Ey0jKy2H60BIDfX9OVqdf3wEtjFpFmTeVDRNzSsqzDTF+6hYqqWtq18WHmuP5c3SPc6lgich5UPkTErZypquXp97byzsaDAFzeJZTXxicRGeRrcTIROV8qHyLiNvKPlZI2L4u8wlJsNph8XXemDOmOp4fN6mgi0gAqHyLiFhZvOsQTy3I5U11LeKCd18b154puYVbHEpELoPIhIs1aRVUNjy/LZUnmYQCu6hbGK+P6Ex5otziZiFwolQ8RabZ2FJSQNi+T3cfL8bDB1Ot7cO813TRmEXFzKh8i0uwYY3jnm4M89d5WHDVOIoPszBqfxMAu7ayOJiKNQOVDRJqVMkcNf1i6hXezjwBwdY9wZt6eSLsAjVlEWgqVDxFpNrYeKWZSRhZ7T5Tj6WHjoWE9+d3gLnhozCLSoqh8iIjljDH871cHePaDbVTVOIkJ9uX11CQGdAy1OpqINAGVDxGxVEllNdP+uYUPtxwFYGjvCF66NZG2bXwsTiYiTaVBX4CQnp7OpZdeSmBgIBEREYwePZq8vLx6+1RWVpKWlka7du0ICAhg7NixFBYWNmpoEWkZcg4VceOsz/hwy1G8PGw8/ove/PevL1HxEGnhGlQ+1q1bR1paGl9++SUff/wx1dXVDBs2jPLy8rp9HnjgAd5//30WLVrEunXrOHLkCGPGjGn04CLivowxvPX5XsbO/YIDpypoH+LHontS+M9BXbDZdHyHSEtnM8aYC/3l48ePExERwbp16xg8eDDFxcWEh4eTkZHBrbfeCsCOHTvo3bs3GzZs4PLLL//ZyywpKSE4OJji4mKCgoIuNJqINFPFFdU8vHgz/9r27Suiw/tG8uLYRIL9vS1OJiIXoyHP3xd1zEdxcTEAoaHfHhS2adMmqqurGTp0aN0+vXr1Ii4u7pzlw+Fw4HA46oUXkZYp68BpJmVkcbjoDD6eHvzhF735dUpHvdoh0spccPlwOp3cf//9XHnllcTHxwNQUFCAj48PISEh9faNjIykoKDgrJeTnp7OjBkzLjSGiLgBYwx//XQvf1qxgxqnoWM7f2ZPSKZfh2Cro4mIBS64fKSlpZGbm8tnn312UQGmTZvG1KlT606XlJQQGxt7UZcpIs3H6fIqHlq0mdU7jgHwi4Ro0sf0I8hXYxaR1uqCysekSZP44IMPWL9+PR06dKg7PyoqiqqqKoqKiuq9+lFYWEhUVNRZL8tut2O365MLRVqijftOMXl+FkeLK/Hx8uDJG/swcWCcxiwirVyD3u1ijGHSpEksXbqUNWvW0Llz53rbBwwYgLe3N6tXr647Ly8vjwMHDpCSktI4iUWk2XM6DW+szWfcX77kaHElXcLasOz3V/LLy3V8h4g08JWPtLQ0MjIyePfddwkMDKw7jiM4OBg/Pz+Cg4O56667mDp1KqGhoQQFBTF58mRSUlLO650uIuL+TpQ5mLpwM+t3HgdgdP8YnrulHwF2faahiHyrQW+1Pde/WN566y3uvPNO4NsPGXvwwQeZP38+DoeD4cOH88Ybb5xz7PJDequtiPv6cs9J7pufxbFSB77eHsy4uS+3XxKrVztEWoGGPH9f1Od8NAWVDxH3U+s0zPkkn1dX7cRpoFtEAHNSk+kZFWh1NBFxEZd9zoeIyLHSSh54J5vP808CcOuADjwzqi/+PvrzIiJnp78OInLBPs8/wZQF2Zwoc+Dn7clzo+MZO6DDz/+iiLRqKh8i0mC1TsNrq3by+if5GAM9IwOZMzGZbhEBVkcTETeg8iEiDVJYUsl987P4au8pACZcFstTN/XF19vT4mQi4i5UPkTkvK3beZwH3snmVHkVbXw8eX5MP0b1b291LBFxMyofIvKzamqd/PnjncxduxuAPtFBzE5Noku4xiwi0nAqHyLyk44UneG++Vls3H8agF9d3pE//KK3xiwicsFUPkTknNbsKGTqws0UVVQTaPfihbEJ/CIh2upYIuLmVD5E5Eeqapy8tHIH//3pXgD6tQ9mdmoSHdu1sTiZiLQEKh8iUs/BUxVMnp9F9sEiAP7jyk48NrIXdi+NWUSkcah8iEidlVsLeHjRZkoqawjy9eKl2xIZ3vf8vpdJROR8qXyICI6aWtI/2sHbX+wDoH9sCLNTk+jQ1t/aYCLSIql8iLRy+0+WMykjiy2HiwH47eAuPDy8J96eHhYnE5GWSuVDpBX7MOcoj/0zh1JHDSH+3sy8PZHrekVaHUtEWjiVD5FWqLK6luc+3Mb/fnkAgEs6tmXWhCRiQvwsTiYirYHKh0grs/dEOWnzMtl2tASA31/TlanX98BLYxYRcRGVD5FW5N3sw0xfsoXyqlratfFh5rj+XN0j3OpYItLKqHyItAJnqmqZ8f5WFnxzEICBnUOZNSGJyCBfi5OJSGuk8iHSwuUfKyVtXhZ5haXYbDD5uu7cd103jVlExDIqHyIt2OJNh3hiWS5nqmsJC7Dz2vj+XNktzOpYItLKqXyItEAVVTU8sWwr/8w8BMCV3drxyrj+RARqzCIi1lP5EGlh8gpKScvIJP9YGR42eGBoD35/bTc8PWxWRxMRAVQ+RFoMYwwLNx7kyXe34qhxEhlk57XxSVzepZ3V0URE6lH5EGkByhw1PL50C8uyjwBwdY9wZt6eSLsAu8XJRER+TOVDxM1tO1LCpIxM9pwox9PDxkPDevK7wV3w0JhFRJoplQ8RN2WMYd5XB3jmg21U1TiJDvbl9QlJXNIp1OpoIiI/SeVDxA2VVFYzbckWPsw5CsCQXhG8fFsibdv4WJxMROTnqXyIuJkth4pJy8jkwKkKvDxsPDayF3dd1RmbTWMWEXEPKh8ibsIYw9+/2MfzH+2gqtZJ+xA/ZqcmkRTX1upoIiINovIh4gaKK6p55J+bWbm1EIBhfSJ56dZEgv29LU4mItJwKh8izVzWgdNMnp/FodNn8PH0YPoNvbjjik4as4iI21L5EGmmjDH8z2d7eWH5DmqchrhQf+akJtOvQ7DV0URELorKh0gzdLq8iocWbWb1jmMA/KJfNOlj+xHkqzGLiLg/lQ+RZmbT/lNMzsjiSHElPl4ePHljHyYOjNOYRURaDJUPkWbC6TS8uX4PL/8rj1qnoXNYG2anJtE3RmMWEWlZVD5EmoGTZQ6mLtzMup3HARjVP4Y/3tKPALseoiLS8ugvm4jFvtpzkvsWZFFY4sDu5cEzo/py+yWxGrOISIul8iFikVqn4Y1P8nll1U6cBrqGt+GNiQPoGRVodTQRkSal8iFigeOlDu5/J4vP808CMDa5A8+O7ou/jx6SItLy6S+diIt9nn+CKQuyOVHmwM/bk2dHx3PrgA5WxxIRcRmVDxEXqXUaXlu9i9fX7MIY6BkZyJyJSXSL0JhFRFoXlQ8RFygsqWTKgiy+3HMKgPGXxvLUTX3x8/G0OJmIiOupfIg0sXU7jzP1nWxOllfRxseT58f0Y1T/9lbHEhGxjMqHSBOpqXUy8+OdvLF2NwC9o4OYk5pEl/AAi5OJiFhL5UOkCRwtPsN987P4Zt9pAH55eRyP/6IPvt4as4iIeDT0F9avX89NN91ETEwMNpuNZcuW1dt+5513YrPZ6v2MGDGisfKKNHtrdhRyw2uf8s2+0wTavZidmsRzo/upeIiI/FuDX/koLy8nMTGR3/zmN4wZM+as+4wYMYK33nqr7rTdbr/whCJuorrWyUsr8/jL+j0A9GsfzOzUJDq2a2NxMhGR5qXB5WPkyJGMHDnyJ/ex2+1ERUVdcCgRd3PodAWTMrLIPlgEwJ1XdGLaDb2we+nVDhGRH2qSYz7Wrl1LREQEbdu25brrruO5556jXbt2Z93X4XDgcDjqTpeUlDRFJJEms3JrAQ8v2kxJZQ1Bvl68eGsiI+JVvkVEzqXRy8eIESMYM2YMnTt3Zvfu3UyfPp2RI0eyYcMGPD1//K/A9PR0ZsyY0dgxRJpcVY2T9OXbeevzfQAkxoYwe0ISsaH+1gYTEWnmbMYYc8G/bLOxdOlSRo8efc599uzZQ9euXVm1ahVDhgz50fazvfIRGxtLcXExQUFBFxpNpEkdOFnBpPmZ5BwqBuDuQZ15eHgvfLwafAy3iEiLUFJSQnBw8Hk9fzf5W227dOlCWFgY+fn5Zy0fdrtdB6SKW/loy1EeXZxDqaOGEH9v/nxbIkN6R1odS0TEbTR5+Th06BAnT54kOjq6qa9KpElVVtfyxw+38/++3A/AJR3bMmtCEjEhfhYnExFxLw0uH2VlZeTn59ed3rt3L9nZ2YSGhhIaGsqMGTMYO3YsUVFR7N69m0ceeYRu3boxfPjwRg0u4kp7T5STNi+TbUe/PSD63mu6MvX6Hnh7aswiItJQDS4fGzdu5Nprr607PXXqVADuuOMO5s6dS05ODn//+98pKioiJiaGYcOG8eyzz2q0Im7r3ezDTF+yhfKqWkLb+DDz9kSu6RlhdSwREbd1UQecNoWGHLAi0pQqq2uZ8f5W5n99EIDLOocya3wSUcG+FicTEWl+mtUBpyLuKP9YGZMyMtlRUIrNBpOv7cZ9Q7rjpTGLiMhFU/kQ+YF/bjrE48tyOVNdS1iAnVfH9eeq7mFWxxIRaTFUPkT+raKqhiff3criTYcAuKJrO14d35+IQI1ZREQak8qHCLCzsJS0eZnsOlaGhw3uH9qDtGu74elhszqaiEiLo/IhrZoxhoUbD/LUe1uprHYSEWhn1oQkLu9y9u8iEhGRi6fyIa1WmaOGx5duYVn2EQAG9whn5u2JhAXobeEiIk1J5UNapW1HSpiUkcmeE+V4eth4cFgP7hncFQ+NWUREmpzKh7Qqxhgyvj7AjPe3UVXjJDrYl1kTkri0U6jV0UREWg2VD2k1SiureWzJFj7MOQrAdb0i+PNtibRt42NxMhGR1kXlQ1qF3MPFpGVksv9kBV4eNh4d0Yu7ruqsMYuIiAVUPqRFM8bw9y/28fxHO6iqddI+xI/XU5NIjmtrdTQRkVZL5UNarOIz1Ty6OIcVWwsAGNYnkpduTSTY39viZCIirZvKh7RI2QeLmJSRyaHTZ/D2tDH9ht7ceUUnbDaNWURErKbyIS2KMYb/+WwvLyzfQY3TEBfqz+zUJBI6hFgdTURE/k3lQ1qMoooqHlq0mVXbjwFwQ78oXhibQJCvxiwiIs2Jyoe0CJv2n2JyRhZHiivx8fLgiRv78MuBcRqziIg0Qyof4tacTsNfPt3DSyvzqHUaOoe1YXZqEn1jgq2OJiIi56DyIW7rZJmDBxdtZm3ecQBuTozh+TH9CLDrbi0i0pzpr7S4pa/2nOS+BVkUljiwe3kw4+a+jLs0VmMWERE3oPIhbsXpNLyxNp+ZH+/EaaBreBvmTEymV1SQ1dFEROQ8qXyI2zhe6mDqwmw+3XUCgDHJ7Xl2VDxtNGYREXEr+qstbuGL/BNMeSeb46UO/Lw9eWZUX267JNbqWCIicgFUPqRZq3UaXlu9i9fX7MIY6BEZwJzUZLpHBlodTURELpDKhzRbhSWVTFmQxZd7TgEw/tJYnrqpL34+nhYnExGRi6HyIc3S+p3HeeCdbE6WV9HGx5Pnx/RjVP/2VscSEZFGoPIhzUpNrZNXVu3kjbW7MQZ6RwcxJzWJLuEBVkcTEZFGovIhzcbR4jPcNz+Lb/adBmDiwDieuLEPvt4as4iItCQqH9IsfLLjGFMXZnO6opoAuxcvjO3HjQkxVscSEZEmoPIhlqqudfLyyjzeXL8HgPj2QcxJTaZjuzYWJxMRkaai8iGWOXS6gsnzs8g6UATAnVd0YtoNvbB7acwiItKSqXyIJf61tYCHF+dQfKaaQF8vXro1gRHx0VbHEhERF1D5EJeqqnGSvnw7b32+D4DE2BBmT0giNtTf2mAiIuIyKh/iMgdOVjBpfiY5h4oBuHtQZx4e3gsfLw+Lk4mIiCupfIhLLN9ylEcW51DqqCHE35uXb01kaJ9Iq2OJiIgFVD6kSVVW1/L8R9v5x4b9AAzo2JZZE5JoH+JncTIREbGKyoc0mb0nypmUkcnWIyUA3HN1Vx4c1gNvT41ZRERaM5UPaRLvbT7C9CVbKHPUENrGh5m3J3JNzwirY4mISDOg8iGNqrK6lhnvb2P+1wcAuKxzKLPGJxEV7GtxMhERaS5UPqTR5B8rY1JGJjsKSrHZYNK13ZgypDteGrOIiMj3qHxIo1iSeYjHl+VSUVVLWIAPr45L4qruYVbHEhGRZkjlQy5KRVUNT727lUWbDgFwRdd2vDquPxFBGrOIiMjZqXzIBdtZWEravEx2HSvDwwZThvRg0nXd8PSwWR1NRESaMZUPaTBjDIs2HuLJ93KprHYSEWjntfFJpHRtZ3U0ERFxAyof0iDljhoeX5bL0qzDAAzqHsYr4/oTFmC3OJmIiLgLlQ85b9uPlpA2L5M9J8rx9LAx9foe3Ht1Vzw0ZhERkQZo8Hsg169fz0033URMTAw2m41ly5bV226M4cknnyQ6Oho/Pz+GDh3Krl27GiuvWMAYw7yv9jNqzufsOVFOVJAvC357OWnXdlPxEBGRBmtw+SgvLycxMZE5c+acdfuLL77IrFmz+K//+i+++uor2rRpw/Dhw6msrLzosOJ6pZXVTJ6fxR+W5lJV4+S6XhF8NGUQl3YKtTqaiIi4qQaPXUaOHMnIkSPPus0Yw6uvvsrjjz/OqFGjAPjHP/5BZGQky5YtY/z48ReXVlwq93AxkzIy2XeyAi8PG4+M6Ml/XtVFr3aIiMhFadRjPvbu3UtBQQFDhw6tOy84OJiBAweyYcOGs5YPh8OBw+GoO11SUtKYkeQCGGP4x4b9/PHD7VTVOmkf4sfrqUkkx7W1OpqIiLQAjVo+CgoKAIiMjKx3fmRkZN22H0pPT2fGjBmNGUMuQvGZah77Zw7Lc7/9/3V9n0heujWBEH8fi5OJiEhLYfmXbkybNo3i4uK6n4MHD1odqdXKPljEL2Z9yvLcArw9bTx5Yx/+8qsBKh4iItKoGvWVj6ioKAAKCwuJjo6uO7+wsJD+/fuf9Xfsdjt2uz4jwkrGGP7ns738acUOqmsNsaF+zJ6QTGJsiNXRRESkBWrUVz46d+5MVFQUq1evrjuvpKSEr776ipSUlMa8KmkkRRVV3P2PTTz34Xaqaw039Iviw/sGqXiIiEiTafArH2VlZeTn59ed3rt3L9nZ2YSGhhIXF8f999/Pc889R/fu3encuTNPPPEEMTExjB49ujFzSyPYtP80kzMyOVJciY+nB0/c2JtfXt4Rm03vZhERkabT4PKxceNGrr322rrTU6dOBeCOO+7g7bff5pFHHqG8vJzf/va3FBUVcdVVV7FixQp8ffUtp82F02n4y6d7eGllHrVOQ6d2/sxOTSa+fbDV0UREpBWwGWOM1SG+r6SkhODgYIqLiwkKCrI6TotzqryKqQuzWZt3HICbE2N4fkw/Auz6pH0REblwDXn+1jNOK/L13lPcNz+LgpJK7F4ePH1zX8ZfGqsxi4iIuJTKRyvgdBreWJvPzI934jTQJbwNc1KT6R2tV5ZERMT1VD5auOOlDqYuzObTXScAGJPUnmdHx9NGYxYREbGInoFasC92n2DKgmyOlzrw9fbg2VHx3HZJrNWxRESklVP5aIFqnYbX1+xi1updOA30iAxgTmoy3SMDrY4mIiKi8tHSHCupZMqCbDbsOQnAuEtiefrmvvj5eFqcTERE5FsqHy3Ip7uO88A72Zwoq8Lfx5Pnb+nH6KT2VscSERGpR+WjBaipdfLqql3MWZuPMdArKpA5E5PpGh5gdTQREZEfUflwc0eLzzBlfjZf7zsFwMSBcTxxYx98vTVmERGR5knlw419suMYUxdmc7qimgC7F+lj+nFTYozVsURERH6Syocbqq518vLKPN5cvweA+PZBzJ6QTKewNhYnExER+XkqH27mcNEZJmdkknmgCIA7r+jEtBt6YffSmEVERNyDyocb+XhbIQ8t2kzxmWoCfb146dYERsRHWx1LRESkQVQ+3EBVjZM/rdjB/3y2F4DEDsHMTk0mNtTf4mQiIiINp/LRzB08VcGkjEw2HyoG4K6rOvPoiF74eHlYnExEROTCqHw0Yytyj/Lw4hxKK2sI9vPm5dsSub5PpNWxRERELorKRzNUWV1L+kfb+fuG/QAkx4Xwemoy7UP8LE4mIiJy8VQ+mpl9J8pJy8hk65ESAH53dRceGtYTb0+NWUREpGVQ+WhG3t98hGlLtlDmqCG0jQ9/vj2Ra3tGWB1LRESkUal8NAOV1bU888E2Mr46AMBlnUKZNSGJqGBfi5OJiIg0PpUPi+0+XkbavEx2FJRis8Gka7sxZUh3vDRmERGRFkrlw0JLsw7xh6W5VFTVEhbgwyvj+jOoe7jVsURERJqUyocFzlTV8tR7uSzceAiAlC7teG18fyKCNGYREZGWT+XDxXYVlvL7eZnsOlaGzQZThnRn8nXd8fSwWR1NRETEJVQ+XMQYw6JNh3jy3Vwqq52EB9p5bXx/rugaZnU0ERERl1L5cIFyRw1PLMtlSdZhAAZ1D+OVcf0JC7BbnExERMT1VD6a2PajJaRlZLLneDkeNnhwWE/uvborHhqziIhIK6Xy0USMMcz/+iAz3t+Ko8ZJVJAvsyYkcVnnUKujiYiIWErlowmUVlYzfWku728+AsC1PcP58+39CW3jY3EyERER66l8NLLcw8VMyshk38kKvDxsPDy8J3cP6qIxi4iIyL+pfDQSYwz/78v9PPfBdqpqnbQP8WPWhCQGdGxrdTQREZFmReWjERSfqWbakhw+2lIAwNDekbx8WwIh/hqziIiI/JDKx0XafLCISfMzOXjqDN6eNh4b2ZvfXNkJm01jFhERkbNR+bhAxhj+9vk+Xli+nepaQ2yoH7MnJJMYG2J1NBERkWZN5eMCFFVU8dCiHFZtLwRgZHwUL4xNINjP2+JkIiIizZ/KRwNt2n+a++ZncbjoDD6eHjx+Y29+dXlHjVlERETOk8rHeXI6Df/96R5eWplHjdPQqZ0/s1OTiW8fbHU0ERERt6LycR5OlVfx4MJsPsk7DsBNiTE8f0s8gb4as4iIiDSUysfP+HrvKe6bn0VBSSV2Lw+euqkvEy6L1ZhFRETkAql8nIPTaZi7bjczP95JrdPQJbwNc1KT6R0dZHU0ERERt6bycRYnyhw88E42n+46AcCYpPY8OzqeNnYtl4iIyMXSs+kPbNh9kikLsjhW6sDX24NnRsVz24AOGrOIiIg0EpWPf6t1Gl5fs4tZq3fhNNA9IoA5E5PpERlodTQREZEWReUDOFZayf0Lsvli90kAbr+kAzNujsfPx9PiZCIiIi1Pqy8fn+06wf3vZHGirAp/H0+eGx3PmOQOVscSERFpsTwa+wKffvppbDZbvZ9evXo19tVctJpaJy+vzONXf/uKE2VV9IoK5L1JV6l4iIiINLEmeeWjb9++rFq16v+uxKt5vcBSUFzJffOz+HrfKQBSB8bx5I198PXWmEVERKSpNUkr8PLyIioqqiku+qJ9kneMBxdu5lR5FQF2L54f04+bE2OsjiUiItJqNEn52LVrFzExMfj6+pKSkkJ6ejpxcXFn3dfhcOBwOOpOl5SUNEUkqmudvPyvPN5ctweAvjFBzElNplNYmya5PhERETm7Rj/mY+DAgbz99tusWLGCuXPnsnfvXgYNGkRpaelZ909PTyc4OLjuJzY2trEjAbB6e2Fd8bgjpSP/vPcKFQ8REREL2IwxpimvoKioiI4dOzJz5kzuuuuuH20/2ysfsbGxFBcXExTUeB9lbozhiXdzubJrGCP7RTfa5YqIiMi3z9/BwcHn9fzd5EeChoSE0KNHD/Lz88+63W63Y7fbmzoGNpuN50b3a/LrERERkZ/W6GOXHyorK2P37t1ER+vVBhEREWmC8vHQQw+xbt069u3bxxdffMEtt9yCp6cnEyZMaOyrEhERETfU6GOXQ4cOMWHCBE6ePEl4eDhXXXUVX375JeHh4Y19VSIiIuKGGr18LFiwoLEvUkRERFqQJj/mQ0REROT7VD5ERETEpVQ+RERExKVUPkRERMSlVD5ERETEpVQ+RERExKVUPkRERMSlVD5ERETEpVQ+RERExKWa/FttG8oYA3z71bwiIiLiHr573v7uefynNLvyUVpaCkBsbKzFSURERKShSktLCQ4O/sl9bOZ8KooLOZ1Ojhw5QmBgIDabrVEvu6SkhNjYWA4ePEhQUFCjXnZLo7U6f1qr86e1On9aq4bRep2/plorYwylpaXExMTg4fHTR3U0u1c+PDw86NChQ5NeR1BQkO6c50lrdf60VudPa3X+tFYNo/U6f02xVj/3isd3dMCpiIiIuJTKh4iIiLhUqyofdrudp556CrvdbnWUZk9rdf60VudPa3X+tFYNo/U6f81hrZrdAaciIiLSsrWqVz5ERETEeiofIiIi4lIqHyIiIuJSKh8iIiLiUq2mfMyZM4dOnTrh6+vLwIED+frrr62O5HJPP/00Nput3k+vXr3qtldWVpKWlka7du0ICAhg7NixFBYW1ruMAwcO8Itf/AJ/f38iIiJ4+OGHqampcfVNaXTr16/npptuIiYmBpvNxrJly+ptN8bw5JNPEh0djZ+fH0OHDmXXrl319jl16hQTJ04kKCiIkJAQ7rrrLsrKyurtk5OTw6BBg/D19SU2NpYXX3yxqW9ao/u5tbrzzjt/dD8bMWJEvX1ay1qlp6dz6aWXEhgYSEREBKNHjyYvL6/ePo31uFu7di3JycnY7Xa6devG22+/3dQ3r1Gdz1pdc801P7pv3XPPPfX2aQ1rNXfuXBISEuo+JCwlJYXly5fXbXeL+5RpBRYsWGB8fHzM3/72N7N161Zz9913m5CQEFNYWGh1NJd66qmnTN++fc3Ro0frfo4fP163/Z577jGxsbFm9erVZuPGjebyyy83V1xxRd32mpoaEx8fb4YOHWqysrLMRx99ZMLCwsy0adOsuDmN6qOPPjJ/+MMfzJIlSwxgli5dWm/7Cy+8YIKDg82yZcvM5s2bzc0332w6d+5szpw5U7fPiBEjTGJiovnyyy/Np59+arp162YmTJhQt724uNhERkaaiRMnmtzcXDN//nzj5+dn3nzzTVfdzEbxc2t1xx13mBEjRtS7n506darePq1lrYYPH27eeustk5uba7Kzs80NN9xg4uLiTFlZWd0+jfG427Nnj/H39zdTp04127ZtM6+//rrx9PQ0K1ascOntvRjns1ZXX321ufvuu+vdt4qLi+u2t5a1eu+998yHH35odu7cafLy8sz06dONt7e3yc3NNca4x32qVZSPyy67zKSlpdWdrq2tNTExMSY9Pd3CVK731FNPmcTExLNuKyoqMt7e3mbRokV1523fvt0AZsOGDcaYb590PDw8TEFBQd0+c+fONUFBQcbhcDRpdlf64ROq0+k0UVFR5qWXXqo7r6ioyNjtdjN//nxjjDHbtm0zgPnmm2/q9lm+fLmx2Wzm8OHDxhhj3njjDdO2bdt6a/Xoo4+anj17NvEtajrnKh+jRo065++01rUyxphjx44ZwKxbt84Y03iPu0ceecT07du33nWNGzfODB8+vKlvUpP54VoZ8235mDJlyjl/p7WulTHGtG3b1vz1r391m/tUix+7VFVVsWnTJoYOHVp3noeHB0OHDmXDhg0WJrPGrl27iImJoUuXLkycOJEDBw4AsGnTJqqrq+utU69evYiLi6tbpw0bNtCvXz8iIyPr9hk+fDglJSVs3brVtTfEhfbu3UtBQUG9tQkODmbgwIH11iYkJIRLLrmkbp+hQ4fi4eHBV199VbfP4MGD8fHxqdtn+PDh5OXlcfr0aRfdGtdYu3YtERER9OzZk3vvvZeTJ0/WbWvNa1VcXAxAaGgo0HiPuw0bNtS7jO/2cee/cT9cq+/MmzePsLAw4uPjmTZtGhUVFXXbWuNa1dbWsmDBAsrLy0lJSXGb+1Sz+2K5xnbixAlqa2vrLTJAZGQkO3bssCiVNQYOHMjbb79Nz549OXr0KDNmzGDQoEHk5uZSUFCAj48PISEh9X4nMjKSgoICAAoKCs66jt9ta6m+u21nu+3fX5uIiIh62728vAgNDa23T+fOnX90Gd9ta9u2bZPkd7URI0YwZswYOnfuzO7du5k+fTojR45kw4YNeHp6ttq1cjqd3H///Vx55ZXEx8cDNNrj7lz7lJSUcObMGfz8/JriJjWZs60VQGpqKh07diQmJoacnBweffRR8vLyWLJkCdC61mrLli2kpKRQWVlJQEAAS5cupU+fPmRnZ7vFfarFlw/5PyNHjqz774SEBAYOHEjHjh1ZuHCh2zzgpPkbP3583X/369ePhIQEunbtytq1axkyZIiFyayVlpZGbm4un332mdVRmr1zrdVvf/vbuv/u168f0dHRDBkyhN27d9O1a1dXx7RUz549yc7Opri4mMWLF3PHHXewbt06q2OdtxY/dgkLC8PT0/NHR/oWFhYSFRVlUarmISQkhB49epCfn09UVBRVVVUUFRXV2+f76xQVFXXWdfxuW0v13W37qftQVFQUx44dq7e9pqaGU6dOtfr169KlC2FhYeTn5wOtc60mTZrEBx98wCeffEKHDh3qzm+sx9259gkKCnK7f1ica63OZuDAgQD17lutZa18fHzo1q0bAwYMID09ncTERF577TW3uU+1+PLh4+PDgAEDWL16dd15TqeT1atXk5KSYmEy65WVlbF7926io6MZMGAA3t7e9dYpLy+PAwcO1K1TSkoKW7ZsqffE8fHHHxMUFESfPn1cnt9VOnfuTFRUVL21KSkp4auvvqq3NkVFRWzatKlunzVr1uB0Ouv+QKakpLB+/Xqqq6vr9vn444/p2bOnW44RztehQ4c4efIk0dHRQOtaK2MMkyZNYunSpaxZs+ZHo6TGetylpKTUu4zv9nGnv3E/t1Znk52dDVDvvtUa1upsnE4nDofDfe5TjXLYajO3YMECY7fbzdtvv222bdtmfvvb35qQkJB6R/q2Bg8++KBZu3at2bt3r/n888/N0KFDTVhYmDl27Jgx5tu3Z8XFxZk1a9aYjRs3mpSUFJOSklL3+9+9PWvYsGEmOzvbrFixwoSHh7eIt9qWlpaarKwsk5WVZQAzc+ZMk5WVZfbv32+M+fattiEhIebdd981OTk5ZtSoUWd9q21SUpL56quvzGeffWa6d+9e7+2jRUVFJjIy0vzqV78yubm5ZsGCBcbf39/t3j76U2tVWlpqHnroIbNhwwazd+9es2rVKpOcnGy6d+9uKisr6y6jtazVvffea4KDg83atWvrvT20oqKibp/GeNx997bIhx9+2Gzfvt3MmTPH7d4++nNrlZ+fb5555hmzceNGs3fvXvPuu++aLl26mMGDB9ddRmtZq8cee8ysW7fO7N271+Tk5JjHHnvM2Gw2869//csY4x73qVZRPowx5vXXXzdxcXHGx8fHXHbZZebLL7+0OpLLjRs3zkRHRxsfHx/Tvn17M27cOJOfn1+3/cyZM+b3v/+9adu2rfH39ze33HKLOXr0aL3L2Ldvnxk5cqTx8/MzYWFh5sEHHzTV1dWuvimN7pNPPjHAj37uuOMOY8y3b7d94oknTGRkpLHb7WbIkCEmLy+v3mWcPHnSTJgwwQQEBJigoCDzH//xH6a0tLTePps3bzZXXXWVsdvtpn379uaFF15w1U1sND+1VhUVFWbYsGEmPDzceHt7m44dO5q77777R0W/tazV2dYJMG+99VbdPo31uPvkk09M//79jY+Pj+nSpUu963AHP7dWBw4cMIMHDzahoaHGbrebbt26mYcffrje53wY0zrW6je/+Y3p2LGj8fHxMeHh4WbIkCF1xcMY97hP2YwxpnFeQxERERH5eS3+mA8RERFpXlQ+RERExKVUPkRERMSlVD5ERETEpVQ+RERExKVUPkRERMSlVD5ERETEpVQ+RERExKVUPkRERMSlVD5ERETEpVQ+RERExKVUPkRERMSl/j9uNngQnXl2jQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#extract the data into an array and then plot the data\n",
    "outdata=np.array(nxs_data['/entry/EXCALIBUR_sum/diff1delta_value_set'].data)\n",
    "plt.plot(outdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67721786-dcde-4826-a970-ccef3850d0d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}